Resultados

Row

Modelo A: mpg ~ wt + disp

Modelo B: mpg ~ wt * disp

Row

R2

78.09

RMSE

2.7765

R2

85.01

RMSE

2.2964

Row

Valores previstos x reais

Frequência dos residuos

Valores previstos x reais

Frequência dos residuos

Row

$call
lm(formula = mpg ~ wt + disp, data = df)

$coefficients
               Estimate  Std. Error   t value     Pr(>|t|)
(Intercept) 34.96055404 2.164539504 16.151497 4.910746e-16
wt          -3.35082533 1.164128079 -2.878399 7.430725e-03
disp        -0.01772474 0.009190429 -1.928609 6.361981e-02

$call
lm(formula = mpg ~ wt * disp, data = df)

$coefficients
               Estimate  Std. Error   t value     Pr(>|t|)
(Intercept) 44.08199770 3.123062627 14.114990 2.955567e-14
wt          -6.49567966 1.313382622 -4.945763 3.216705e-05
disp        -0.05635816 0.013238696 -4.257078 2.101721e-04
wt:disp      0.01170542 0.003255102  3.596022 1.226988e-03

Analise Exploratória

Row

Distribuição de frequência por MPG

Boxplot do atributo MPG por CYL

Relação entre o atributo MPG x WT

Análise de Correlação

Row

Estatística Descritiva

Atributo mean sd median trimmed mad min max range skew kurtosis se
mpg 20.090625 6.0269481 19.200 19.696154 5.4114900 10.400 33.900 23.500 0.6106550 -0.3727660 1.0654240
cyl* 2.093750 0.8929608 2.000 2.115385 1.4826000 1.000 3.000 2.000 -0.1746119 -1.7621198 0.1578547
disp 230.721875 123.9386938 196.300 222.523077 140.4763500 71.100 472.000 400.900 0.3816570 -1.2072119 21.9094727
hp 146.687500 68.5628685 123.000 141.192308 77.0952000 52.000 335.000 283.000 0.7260237 -0.1355511 12.1203173
drat 3.596563 0.5346787 3.695 3.579231 0.7042350 2.760 4.930 2.170 0.2659039 -0.7147006 0.0945187
wt 3.217250 0.9784574 3.325 3.152692 0.7672455 1.513 5.424 3.911 0.4231465 -0.0227108 0.1729685
qsec 17.848750 1.7869432 17.710 17.827692 1.4158830 14.500 22.900 8.400 0.3690453 0.3351142 0.3158899
vs* 1.437500 0.5040161 1.000 1.423077 0.0000000 1.000 2.000 1.000 0.2402577 -2.0019376 0.0890983
am* 1.406250 0.4989909 1.000 1.384615 0.0000000 1.000 2.000 1.000 0.3640159 -1.9247414 0.0882100
gear* 1.687500 0.7378041 2.000 1.615385 1.4826000 1.000 3.000 2.000 0.5288545 -1.0697507 0.1304266
carb* 2.718750 1.3733494 2.000 2.653846 1.4826000 1.000 6.000 5.000 0.3504105 -0.9467971 0.2427762

Dataset

Dataset MTCARS

---
title: "MTCARS"
author: "Stefani Rmalho"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    theme: cerulean 
    source_code: embed
    social: menu
---

```{r setup, include=FALSE}
### Fonte de pesquisa ##
# Documentacao do flexdashboard
# https://rmarkdown.rstudio.com/flexdashboard/index.html

### referencias ###
# https://r4ds.had.co.nz/
# Garrett Grolemund
# Hadley Wickham

# Carregando os modulos
library(ggplot2)
library(dplyr)
library(tibble)
library(ggcorrplot) # Plotar as correlacoes
library(DT) # Imprimir o dataset permitindo interacoes
library(psych) # Funcao describe
library(knitr) # Imprimir tabelas em um formato mais amigavel
library(modelr) 
library(flexdashboard)

# Criando um dataset os dados mtcars
df <- datasets::mtcars %>%
  rownames_to_column() %>%
  as_tibble() %>%
  mutate(vs = factor(vs),
         am = factor(am),
         gear = factor(gear),
         carb = factor(carb),
         cyl = factor(cyl))

# Tema dos graficos
theme_dahs <- theme_light() +
  theme(panel.grid.minor.x = element_blank(),
  panel.grid.major.x = element_blank(),
  panel.grid.minor.y = element_blank())

# Criando os modelos de regressao
model_lm_1 <- lm(mpg ~ wt + disp,df)
model_lm_2 <- lm(mpg ~ wt * disp,df)
```

Resultados {data-icon="fas fa-chart-line"}
===================================== 

Row {data-height=20}
-----------------------------------------------------------------------
### Modelo A: mpg ~ wt + disp {.no-mobile}

### Modelo B: mpg ~ wt * disp {.no-mobile}

Row {data-height=130}
-----------------------------------------------------------------------

### R2

```{r}
# calculando o R2 do modelo 1
r2_1 <- round(rsquare(model_lm_1,df) * 100,2)

valueBox(r2_1, 
         icon = "fas fa-percent",
         color = "primary")
```

### RMSE

```{r}
# calculando o RMSE do modelo 1
rmse_1 <- round(rmse(model_lm_1,df),4)

valueBox(rmse_1, 
         icon = "fas fa-chart-area",
         color = "primary")
```

### R2

```{r}
# calculando o R2 do modelo 2
r2_2 <- round(rsquare(model_lm_2,df) * 100,2)
valueBox(r2_2, 
         icon = "fas fa-percent",
         color = "primary")

```

### RMSE

```{r}
# calculando o RMSE do modelo 2
rmse_2 <- round(rmse(model_lm_2,df),4)

valueBox(rmse_2, 
         icon = "fas fa-chart-area",
         color = "primary")

```

Row {data-height=500}
-----------------------------------------------------------------------

### Valores previstos x reais

```{r}
# Hitograma valores previstos x mpg
p5 <- df %>% 
  add_predictions(model_lm_1) %>% 
  ggplot() +
  geom_histogram(aes(x = mpg, bins = 10), size = 1, fill = "sky blue") +
  geom_histogram(aes(x = pred, bins = 10), size = 1, fill = "orange") +
  theme_dahs

p5
```

### Frequência dos residuos

```{r}
# grafico com a frequencia dos residuos
p6 <- df %>% 
  add_residuals(model_lm_1) %>% 
  ggplot(aes(x = resid)) +
  geom_freqpoly(bins = 40, color = "blue", size = 1) +
  theme_dahs

p6
```

### Valores previstos x reais

```{r}
# Hitograma valores previstos x mpg
p7 <- df %>% 
  add_predictions(model_lm_2) %>% 
  ggplot() +
  geom_histogram(aes(x = mpg, bins = 10), size = 1, fill = "sky blue") +
  geom_histogram(aes(x = pred, bins = 10), size = 1, fill = "orange") +
  theme_dahs

p7
```

### Frequência dos residuos

```{r}
# grafico com a frequencia dos residuos
p8 <-  df %>% 
  add_residuals(model_lm_2) %>% 
  ggplot(aes(x = resid)) +
  geom_freqpoly(bins = 40, color = "blue", size = 1) +
  theme_dahs

p8
```

Row {data-height=350}
-----------------------------------------------------------------------

### {.no-mobile}

```{r}
# resudo do modelo
p9 <- summary(model_lm_1)[c(1,4)]
p9
```
### {.no-mobile}

```{r}
# resumo do modelo
p10 <- summary(model_lm_2)[c(1,4)]
p10
```

Analise Exploratória {data-icon="fa-signal"}
===================================== 

Row {data-height=500}
-----------------------------------------------------------------------

### Distribuição de frequência por MPG

```{r}
# Plot p0 - Histograma do atributo mpg
p0 <- ggplot(df, aes(x = mpg)) +
  geom_histogram(bins = 10, fill = "SKY blue") +
  theme_dahs

p0
```

### Boxplot do atributo MPG por CYL

```{r}
# Plot p1 - Box plot mpg x cyl
p1 <- ggplot(df, aes(x = cyl, y = mpg)) +
  geom_boxplot(fill = "orange", color = "orange3") +
  theme_dahs

p1
```

### Relação entre o atributo MPG x WT

```{r}
# Plot p1 - scatterplot mpg x wt
p2 <- ggplot(df, aes(x = mpg, y = wt)) +
  geom_point(size = 4, color = "red") +
  geom_point(size = 3, color = "orange") +
  geom_smooth(method = lm, se = FALSE, color = "grey2") +
  theme_dahs

p2
```

### Análise de Correlação

```{r}
# Plot p3 - correlacoes
p3 <- df %>% select_if(is.numeric) %>%
  cor() %>%
  ggcorrplot(hc.order = TRUE,
             type = "lower",
             outline.color = "white")

p3
```

Row {data-height=500}
-----------------------------------------------------------------------


### Estatística Descritiva {.no-mobile}

```{r}
# plot p4 - tabela estatistica
p4 <- df %>%
  describe() %>%
  data.frame() %>%
  rownames_to_column() %>%
  rename(Atributo = rowname) %>%
  select(-vars, -n) %>%
  slice(-1) %>%
  kable()

p4
```



Dataset {data-icon="fa-table"}
=====================================   

### Dataset MTCARS {.no-mobile}
    
```{r}
# Tabela
t <- datatable(df)
t